Method for rendering 2d and 3d data within a 3d virtual environment

ABSTRACT

One variation of a method for modeling a human body includes: driving a sensor block along a path above a platform occupied by a user and recording a sequence of depth images and color images, via the sensor block, at each capture position in a sequence of capture positions along the path; compiling the set of depth images into a low-density 3D model of the user and a high-density 3D model of the user; extracting a set of color patches, from the sequence of color images, corresponding to discrete regions on a surface of the low-density 3D model of the user; projecting the set of color patches onto the surface of the low-density 3D model to generate a 3D color model of the user; and extracting a value of a dimension, projected from the low-density 3D color model onto the high-density 3D model, from the high-density 3D model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a continuation application of U.S. patentapplication Ser. No. 16/796,816, filed on 20 FEB. 2020, which is acontinuation application of U.S. patent application Ser. No. 16/408,291,filed on 9 MAY 2019, which is a continuation-in-part application of U.S.patent application Ser. No. 15/612,013, filed on 2 JUN. 2017, whichclaims the benefit of U.S. Provisional Application No. 62/345,787, filedon 4 JUN. 2016, each of which is incorporated in its entirety by thisreference.

TECHNICAL FIELD

This invention relates generally to the field of 3D scanning and morespecifically to a new and useful method for rendering 2D and 3D datawithin a 3D virtual environment in the field of 3D scanning.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A and 1B are flowchart representations of a method;

FIG. 2 is a flowchart representation of one variation of the method;

FIG. 3 is a schematic representation of one variation of the method;

FIG. 4 is a schematic representation of one variation of the method;

FIG. 5 is a flowchart representation of one variation of the method;

FIG. 6 is a flowchart representation of one variation of the method;

FIG. 7 is a flowchart representation of one variation of the method;

FIG. 8 is a flowchart representation of one variation of the method; and

FIG. 9 is a flowchart representation of one variation of the method.

DESCRIPTION OF THE EMBODIMENTS

The following description of embodiments of the invention is notintended to limit the invention to these embodiments but rather toenable a person skilled in the art to make and use this invention.Variations, configurations, implementations, example implementations,and examples described herein are optional and are not exclusive to thevariations, configurations, implementations, example implementations,and examples they describe. The invention described herein can includeany and all permutations of these variations, configurations,implementations, example implementations, and examples.

1. First Method

As shown in FIGS. 1A and 1B, a method S100 for modeling a human bodyincludes: initiating a first scan cycle while a user occupies a scaleplatform at a first time in Block S110; during the first scan cycle,driving a depth sensor along a path above the platform and about theuser in Block S120; recording a first sequence of depth images, via thedepth sensor, at each capture position in a first sequence of capturepositions at a first density along the path in Block S130; calculating afirst set of transforms that align the first sequence of depth images inBlock S140; and compiling the first set of depth images according to thefirst set of transforms to generate a first three-dimensional(hereinafter “3D”) model of the user for the first time in Block S150.The method S100 also includes: initiating a second scan cycle while theuser occupies the scale platform at a second time succeeding the firsttime in Block S112; during the second scan cycle, driving the depthsensor along the path in Block S122; recording a second sequence ofdepth images, via the depth sensor, at each capture position in a secondsequence of capture positions at a second density less than the firstdensity along the path in Block S132; and compiling the second set ofdepth images according to the first set of transforms to generate asecond 3D model of the user for the second time in Block S152.

One variation of the method S100 shown in FIGS. 1A, 1B, and 8 includes:during the second scan cycle, driving the depth sensor along the path inBlock S122; recording a second sequence of depth images, via the depthsensor, corresponding to a second sequence of capture positions at thefirst density along the path in Block S132; and compiling a subset ofthe second set of depth images according to the first set of transformsto generate a second 3D model of the user for the second time in BlockS152.

Another variation of the method S100 shown in FIGS. 8 and 9 includes:initiating a scan cycle while a user occupies a scale platform in BlockS110; during the scan cycle, driving a sensor block along a path abovethe platform and about the user in Block S120; recording a sequence ofdepth images and a sequence of color images, via the sensor block, ateach capture position in a sequence of capture positions along the pathin Block S130; compiling the set of depth images into a low-density 3Dmodel of the user in Block S150; compiling the set of depth images intoa high-density 3D model of the user in Block S160; extracting a set ofcolor patches, from the sequence of color images, corresponding to a setof discrete regions on a surface of the low-density 3D model of the userin Block Sil o; projecting the set of color patches onto the surface ofthe low-density 3D model to generate a 3D color model of the user inBlock S172; receiving selection of a dimension on the low-density 3Dcolor model in Block S180; extracting a value of the dimension,projected from the low-density 3D color model onto the high-density 3Dmodel, from the high-density 3D model in Block S182; and returning thevalue of the dimension in Block S184.

2. Applications

Generally, Blocks of the method S100 can be executed by a scanningsystem, a remote computer system, and/or a local computing device: toleverage computation power to transform scan data collected during afirst scan cycle into a first 3D color model defining a high accuracy,high-resolution representation of the unique surface anatomy of a user;and to then leverage functions for compiling scan data from this firstscan cycle to transform similar scan data recorded during a later scancycle into a similarly-accurate, similarly-high-resolution 3D colormodel of the user but in less time and/or with the limited computationalpower of the local computing device.

2.1 Efficient Generation of 3D Color, Texturized Models

More specifically, Blocks of the method S100 can be executed by scanningthe system to record a first sequence of depth images (e.g., depth mapsor 3D point clouds) and color images (e.g., two-dimensional—orhereinafter “2D”—color photographic images) while scanning a sensorblock (e.g., containing a depth sensor and a color camera) around a userduring a first scan cycle in which the user occupies the scanningsystem. A remote computer system can then execute other Blocks of themethod S100 to: access the first sequence of depths maps and colorimages; calculate a first set of transforms (or “maps,” 3Dtransformations) that align surfaces represented by points in thesedepth images; compile some or all of these depth images into a 3D modelthat represents the surface anatomy of the user with a high degree ofaccuracy; and extract a first set of color patches—from this firstsequence of color images—that depict discrete areas on the user. Theremote computer system can then render the first set of color patchesover corresponding regions on the surface of the first 3D model of theuser to generate a first 3D color image of the user and return thisfirst 3D color model to the user's computing device (e.g., a tablet, asmartphone, a laptop) for display within a native application or webbrowser. The remote computer system can also: estimate different skin,hair, and other surface types and textures (e.g., cellulitis, eczema) onthe user based on features detected in these color patches; retrieve 2Dor 3D texture models for these various texture types; and project these2D or 3D texture models onto these color patches or onto the 3D modelitself, respectively, in order to further augment authenticity of thisfirst 3D color model of the user.

In particular, the scanning system can cooperate with the remotecomputer system to leveraging high-bandwidth connectivity and extensivecomputing resources on a computer network to quickly compile scan datafrom a first scan of a user into a high-resolution, high-accuracy 3Dcolor model of the user to then return this 3D color model to a localcomputing device (e.g., the user's smartphone) for immediatepresentation to the user, thereby providing the user rapid feedbackfollowing a first interaction with the scanning system. (Alternatively,the local computing device can access and render the first 3D model andthe first set of color patches locally before displaying the resulting3D color model for the user.)

However, recalculating new transforms to align scan data collected bythe scanning system during a next scan cycle may be similarlycomputationally expensive and may therefore require that these data beuploaded to the remote computer system for remote processing, andoffloading these scan data may again be bandwidth intensive. Therefore,during a second cycle with the user—such as hours, days, or weeks afterthe first scan cycle with the user—the scanning system can: drive thesensor block along the same or similar path; capture a second sequenceof depth images and color images at a lower density of positions alongthe path; and offload these scan data to the user's local computingdevice. A native or browser-based application executing on the localcomputing device can then: retrieve the first set of transformscalculated for the first (i.e., earlier) scan cycle, which may besubstantially unique to the user due to the user's height and geometry;initialize alignment of this second sequence of scan depth images basedon first set of transforms; refine these transforms to minimize spatialerror between surfaces represented by points in this second sequence ofdepth images; and then compile these depth images into a second 3D modelof the user. The native or browser-based application executing on thelocal computing device can also retrieve crop areas and coordinates forthe first set of color patches generated for the first 3D color model;transfer these crop areas to the second sequence of color imagesrecorded during the scan to generate a second set of color patches; andlocate (or “render”) this second set of color patches on the second 3Dmodel of the user based on corresponding coordinates from the first setof color patches. In particular, because the first set of transformsgenerated during the first scan cycle predict alignment of the secondset of depth images from this second scan cycle, the scanning system canrecord a lower density of depth images that yield less overlap betweenadjacent depth images during this second scan cycle, and the localcomputing device can leverage the first set of transforms to calculateinitial alignment between these depth images with low or no errordespite this reduced overlap between adjacent depth images. Similarly,because color patch definitions were already generated for color imagesrecorded at a small number of positions along the path during the firstscan cycle, the scanning system can record a small number of colorimages at these same positions along the path during this second scancycle, and the local computing device can leverage color patchdefinitions from the first scan cycle to extract a second set of colorpatches directly from color images recorded during this second scancycle. The local computing device can similarly port texture models fromlocations in the first set of color patches or regions of the first 3Dmodel onto corresponding locations in the second set of color patches oronto corresponding regions of the second 3D model in order to simplifytexture augmentation of the second 3D model while limiting oreliminating further processing of these scan data to predict skin, hair,and other surfaces types on the user during the second scan cycle.

Therefore, the native or browser-based application executing on thelocal computing device can implement Blocks of the method S100 toleverage functions generated by the remote computer system during thefirst scan cycle: to enable rapid local reconstruction of a 3D colormodel from scan data recorded during a later scan cycle; and reduceprocessing time by eliminating upload of raw scan data from the scanningsystem to the remote computer system and download of this later 3D colormodel from the remote computer system to the local computing device.

Alternatively, the remote computer system can implement Blocks of themethod S100 to leverage functions generated by the remote computersystem during the first scan cycle to simplify reconstruction of a 3Dcolor model from scan data recorded during a later scan cycle and toreduce latency from recordation of these scan data to presentation of anew 3D color model for the user, such as: by selectively uploading alimited number of depth images from the scanning system necessary toconstruct a new 3D model of the user based on transforms generatedduring the first scan cycle; by selectively uploading a limited numberof color images from the scanning system based on color patchdefinitions generated during the first scan cycle; by selectivelycompiling these new scan data into a new 3D color model of the userbased on the transforms and color patch definitions generated during thefirst scan cycle.

Therefore, the scanning system, remote computer system, and/or localcomputing device can execute Blocks of the method S100 to generate afirst high-resolution, high-accuracy, color- and texture-realistic 3Dmodel of a user based on scan data recorded during a first scan cyclewhen the user first engages with the scanning system. The scanningsystem, remote computer system, and/or local computing device can laterexecute Blocks of the method S100: to record lower-density scan data ofthe user during a second, later scan cycle; and to generate a second 3Dmodel of a user that is similarly high-resolution, high-accuracy,color-realistic, and texture-realistic based on this lower-density scandata and with lower bandwidth requirements, reduced processing time,and/or reduced processing load by leveraging results of the first 3Dmodel of the user.

2.2 User-Facing and Backend Models

Furthermore, in addition to generating a user-facing 3D color,texturized model of the user based on scan data recorded during a scancycle, the remote computer system (or the local computing device) canalso transform these same depth images into a backend 3D model of theuser defining a higher-density, higher-accuracy 3D mesh. Later, thelocal computing device (or the scanning system or other local device)can display this 3D color, texturized model of the user and record aselection of a dimension on this 3D color, texturized model from theuser, such as: total height; waist circumference; neck circumferenceshoulder width; or bicep circumference. The local computing device (orthe remote computer system) can then: transfer this dimension onto thebackend 3D model; extract a value of this dimension from the backend 3Dmodel; and return this value to the user.

The remote computer system and/or the local computing device cantherefore: generate a user-facing 3D color, texturized model paired witha backend 3D model defining a higher-mesh density based on the same scandata from one scan cycle; render and display the user-facing 3D color,texturized model, which may define a more compelling and visuallyauthentic representation of the user that the backend 3D model; andextract dimensions—selected at the user-facing 3D color, texturizedmodel—from the backend 3D model, which may define a higher-accuracyrepresentation of the user.

3. Scanning System

Generally, Blocks of the method S100, can be executed by and inconjunction with a scanning system 100, as shown in FIGS. 2, 3, 4, and5. The scanning system 100 can include: a platform 110; a sensor block120 containing a depth sensor 122 and a color camera 124; a boom 130supporting the sensor block 120; and an actuation system 140 configuredto sweep the sensor block 120 along a path (e.g., a helical path) abovethe platform 110.

In one implementation, the platform 110 defines a step surfaceconfigured to support a human and contains graphics instructing footorientation on the scanning system 100. In this implementation, theplatform 110 is coupled to a scale 112 configured to output a weight ormass of a human occupying the platform 110. For example, the scale 112can include a load cell supporting the platform 110 above. The platform110 can additionally or alternatively a bioimpedance contact sensorconfigured to measure bioimpedance across a human's feet when standingon the platform 110.!

The boom 130 is coupled to the platform 110 and is configured to supportthe sensor block 120. For example, the boom 130 can include a horizontalsegment 132 extending laterally (or configured to extend laterally) fromthe platform 110 and configured to rotate about a vertical axis throughthe platform 110. The boom 130 can also include a vertical segment 134extending upwardly (or configured to extend upwardly) from the distalend of the horizontal segment 132 of the boom 130.

The sensor block 120 is coupled to the distal end of the verticalsegment 134 of the boom 130 and includes a suite of sensors. Forexample, the sensor block 120 can include a depth sensor 122—such as inthe form of a structured light 3D scanner or a pair of calibratedstereoscopic cameras—facing the vertical axis of the platform 110 andconfigured to output a depth image containing a 3D point cloudrepresenting discrete points on surfaces in its field of view. Thesensor block 120 can also include a color camera 124—such as an RGB CMOSor CCD camera—similarly facing the vertical axis of the platform no andconfigured to output a 2D color image. The depth sensor 122 and thecolor camera can also be time-synchronized to record concurrent depthimage and color image pairs during operation, thereby enablingreconstruction of a 3D color model from color images based on calculatedtransforms that aligned concurrent depth images and a known offsetbetween the depth sensor 122 and the color camera 124. The sensor block120 can further include an inertial measurement unit (or “IMU”)configured to output linear accelerations and rotational velocities ofthe sensor block 120 throughout a scan cycle. (Alternatively, the sensorblock 120 can include a discrete accelerometer and separate gyroscope.)However, the sensor block 120 can include any other quantity and type(s)of sensors.

The actuation system 140 can include: an azimuthal actuator configuredto rotate the horizontal segment 132 of the boom 130—and therefore thevertical segment 134 of the boom 130 and the sensor block 120—about thevertical axis of the platform 110; an altitude actuator configured toextend and retract the vertical segment 134 of the boom 130 in order toraise and lower the sensor block 120; and a pitch actuator coupled to(e.g., interposed between) the vertical segment 134 of the boom 130 andthe sensor block 120 and configured to sweep the sensor block 120through a range of pitch (and yaw) positions relative to the verticalsegment 134 of the boom 130. The actuation system 140 can also include aset of position sensors—such as optical linear and rotaryencoders—configured to output signals indicating absolute position orrelative changes in position of the boom and sensor block 120, such asalong and about axes shown in FIG. 3.

The scanning system 100 can also include a controller 150 configured tocoordinate the actuation system 140 to rotate the sensor block 120through a path (e.g., a helical path) and to trigger the depth sensor122 and the color camera 124 to record depth image and color image pairsduring a scan cycle. The scanning system 100 can similarly include localmemory 152 configured to store local copies of depth images and colorimages recorded during a scan cycle, such as prior to offloading thesescan data upon conclusion of the scan cycle. The scanning system 100 canalso include a wireless communication module 154 configured to uploaddepth images and color images—from local memory 152—to the remotecomputer system, such as by broadcasting these scan data directly via alocal gateway or uploading these scan data to the remote computer systemvia a user's local computing device. For example, the controller 150,local memory 152, and the wireless communication module 154 can bearranged in the sensor block 120 or in a base under the platform no.

In one variation, the scanning system 100 includes a mobile device mountat the end of the vertical segment 134 of the boom 130—in place of thesensor block 120. The controller in the scanning system 100 can thenselectively trigger a mobile device (e.g., a smartphone, a tablet)—wheninstalled in the mobile device mount—to capture depth images and/orcolor images as the scanning system 100 executes a scan cycle.Alternatively, in the variation, a controller in the mobile device canserve commands to actuators in the scanning system 100 drive the mobiledevice mount through a path during a scan cycle and can locally capturedepth images and color images accordingly.

The scanning system 100 can also include a power supply, such as in theform of a battery and/or a power regulator arranged in the base underthe platform 110.

In one variation, other Blocks of the method S100 are executed by theremote computer system, such as a computer network or remote server, totransform scan data into a 3D color, texturized model of a user. Blocksof the method S100 can additionally or alternatively be executed by anative application or web-browser executing on a local computing device(e.g., a smartphone, a tablet, a laptop computer) to transform scan datainto a 3D color, texturized model of a user, such as with the benefit oftransforms, color patch, and texture data generated by the remotecomputer system based on scan data from earlier scan of the user.

4. First Scan

Blocks S110, S120, and S130 of the method S100 recite: initiating afirst scan cycle while a user occupies a scale platform at a first time;driving a depth sensor along a path above the platform and about theuser during the first scan cycle; and recording a first sequence ofdepth images via the depth sensor and/or color images via the colorcamera at each capture position in a first sequence of capture positionsat a first density along the path, respectively. Generally, in BlockS110, the scanning system can initiate a new scan cycle with a user,such as when the user steps on the platform or manually triggers a newscan cycle at her local computing device. During this scan cycle, thecontroller can coordinate the actuator to scan the sensor block aroundthe user—now standing on the platform—in Block S120 while the depthsensor and the color camera record depth images and color images of theuser, respectively, in Block S130. In particular, the scanning systemcan execute a new scan cycle to autonomously record a sequence of depthimages and color images of the user in Blocks S110, S120, and S130. Theremote computer system can later access these scan data to automaticallyconstruct an authentic, color-true, texturized representation of theuser.

In one example shown in FIGS. 1A and 1B, the scanning system drives theactuation system to sweep the sensor block about a preset (or “fixed,”preplanned, static) path centered about the vertical axis of theplatform and rising from proximal the platform to a maximum height oftwo meters above the platform while the user occupies (i.e., stands on)the platform. In this example, as the sensor block sweeps along thispath, the depth sensor can record a sequence of depth images, and thecolor camera can record a concurrent sequence of color images.

In another example shown in FIG. 6, the scanning system can drive theactuation system to sweep the sensor block: from a low pitch angle tolocate the user's feet near the centers of the fields of view of thedepth sensor and the color camera; to a high pitch angle to locate theuser's head near the centers of the fields of view of the depth sensorand the color camera. In the foregoing example, as the actuation systemraises the sensor block toward its maximum height of two meters (or asthe controller detects the user's head in depth images or color imagesrecorded by the depth sensor or the color camera in near real-timeduring the scan cycle), the actuation system can pitch the sensor blockdownward, thereby enabling the depth sensor and the color camera torecord depth images and color images, respectively, of the top of theuser's head. In another example, the actuation system can drive thesensor block from an initial height proximal the platform to a maximumheight one meter above the platform while rotating the boom about thevertical axis through the platform and holding the sensor block at acontact pitch angle, thereby sweeping the sensor block through a path.In this example, once the sensor block reaches the maximum height, theactuation system can slowly pitch the sensor block upwardly toward theuser's head while continuing to rotate the boom about the vertical axis,thereby limiting the maximum height of the scanning system during thisscan cycle while also enabling the depth sensor and the color camera tocapture scan data of the user's upper torso and head. However, thescanning system can sweep the sensor block along a path of any othergeometry during a scan cycle.

(In another example shown in FIG. 7 , the scanning system is mountedhorizontally on a wall and sweeps the sensor block around a user's headduring a scan cycle. The remote computer system or the local computingdevice can then implement methods and techniques described below totransform depth images and color images recorded during this scan cycleinto a 3D color, texturized model of the user's head or face morespecifically.)

4.1 Predefined Waypoints

In one implementation shown in FIG. 1, the scanning system drives thesensor block through a predefined sequence of waypoints and records adepth image at each waypoint in this sequence during the first scancycle. In this implementation, each waypoint can define a combination ofazimuth, altitude, and pitch positions of the sensor block. When thesensor block occupies an azimuth, altitude, and pitch position definedby waypoints in this sequence, the field of view of the depth sensorcan: overlap the field of view of the depth sensor when occupying apreceding waypoint by more than a minimum degree of overlap (e.g., 50%);and overlap the field of view the depth sensor when occupying anotherwaypoint—one altitude level down—by more than a minimum degree ofoverlap. More specifically, the predefined sequence of waypoints candefine a high density of positions for depth image capture around theuser such that depth images recorded during this first scan cyclecontain data overlap spatially by a minimum proportion (e.g., 50%),thereby ensuring that these depth images contain sufficient pointdensity for proper alignment between adjacent depth images and thusenabling the remote computer system to subsequently align these depthimages with high precision, verify that this alignment exhibits minimumerror, and thus generate a high-accuracy, high-resolution 3D model ofthe user from these data.

For example, the sequence of waypoints can define azimuth, altitude, andpitch offsets between consecutive waypoints that correspond: to a targetlateral overlap of 50%—at a plane intersecting the vertical axis of theplatform—between laterally-adjacent depth images recorded at twoconsecutive waypoints along the path; and a target vertical overlap of50%—at the plane intersecting the vertical axis of the platform—betweentwo depth images recorded at (approximately) the same azimuthal positionduring two consecutive rotations of the boom. In another example, thesequence of waypoints can define azimuth, altitude, and pitch offsetsbetween consecutive waypoints predicted to yield: a target lateraloverlap of 40%—at a plane offset from the vertical axis of the platformtoward the sensor block by a distance of 250 millimeters (e.g., anaverage maximum radius of a human body)—between laterally-adjacent depthimages recorded at two consecutive waypoints along the path.

Thus, in this implementation, the scanning system can implementclosed-loop controls—based on position, linear acceleration, and/orangular velocity measurements output by sensors in the scanningsystem—to step the sensor block through consecutive waypoints or tocontinuously sweep the sensor block through this sequence of waypoints.When the sensor block occupies (or falls very near) each of thesewaypoints, the scanning system can trigger the depth sensor to record adepth image and trigger the color camera to record a corresponding colorimage. The scanning system can also tag each of these depth images andcolor images with: a timestamp; a waypoint identifier; a scan cycleidentifier; actual azimuth and altitude positions of the sensor blockwhen these data were collected; and/or concurrent linear accelerationand angular velocity values output by the IMU when these data werecollected; etc.

4.2 Free Record

In another implementation, the scanning system: sweeps the sensor block(approximately) along a path, such as a helical path rising around theuser standing on the platform; and triggers the depth sensor and colorcamera to record depth image and color image pairs at a regular intervalduring this scan cycle, such as at a constant preset frequency (e.g., 30Hz) or at a frequency proportional to an angular speed of the sensorblock.

In this implementation, the computer system can tag each of these depthimages and color images with: a timestamp; a scan cycle identifier;actual azimuth and altitude positions of the sensor block when thesedata were collected; and/or concurrent linear acceleration and angularvelocity values output by the IMU when these data were collected; etc.Later, the scanning system (or the remote computer system, etc.) candefine a sequence of waypoints for a next scan cycle with the user atthe scanning system based on these metadata. In particular, dynamiccharacteristics of a sensor block at the end of a boom within a unit ofthe scanning system may be sensitive to manufacturing tolerances atjoints in the boom, calibration of actuator and sensors in the actuationsystem, and small variation in weight of the sensor block such thatmotion and oscillation of sensor blocks in multiple units of thescanning system differ significantly when driven along identical targetpaths. Therefore, (an instance of) the scanning system can: drive thesensor block along a path (e.g., a helical path) during a first scancycle; record a sequence of depth images and color images at unplannedlocations along this path; and record metadata for positions,orientations, and motion of the sensor block when each of these depthimage and color image pairs were recorded, which may capture uniquedynamic characteristics of the sensor block. Later, the computer systemgenerates a set of transforms that align these depth images and thenimplements these transforms to construct a 3D color model of the userfrom a subset of these depth image and color image pairs based on thesetransforms, as described below. The computer system (or the scanningsystem, the local computing device) can then also: retrieve azimuth andaltitude positions of the sensor block stored in each depth image inthis subset of depth images; generate a waypoint for each of these depthimages based on these azimuth and altitude position data; define anorder for these waypoints; and store these waypoints in association withthe user and/or the scanning system. Later, the scanning system canimplement closed-loop controls—as described above—to drive the sensorblock to each waypoint in this sequence and to record depth image andcolor image pairs at each of these waypoints during a next scan cycle.

Therefore, the scanning system and the computer system can implementthis process to calibrate a set of waypoints to dynamic characteristicsof the scanning system based on a “free scan” process during the firstscan cycle at the scanning system. The scanning system can then executethis calibrated set of waypoints during later scan cycles to capturescan data of the user, which may leverage natural dynamiccharacteristics of the scanning system to capture scan data athighly-consistent locations and reduce spatial error between capturepositions between the first scan cycle and later scan cycles. Becausecapture positions in these later scan cycles may exhibit high spatialsimilarity to (many or all of) the capture positions executed in thefirst scan cycle, the local computing device (or the remote computersystem) can implement the same transforms generated for scan datacollected during the first scan cycle to initialize alignment of scandata collected during these later scan cycles.

However, the scanning system can implement any other control and datacapture schema during the first scan cycle.

4.3 Adaptive Scan Parameters: User Height

In one variation, the scanning system can adjust the path of the sensorblock during the first scan cycle based on the user's height.

In one implementation, the scanning system: drives the depth sensoralong a first, predefined path in Block S120; records a first sequenceof depth images and a first sequence of color images along the firstpath; implements computer vision techniques (e.g., template matching,object detection) to detect a lower leg feature (e.g., a knee) offsetabove the platform in the first subset of depth images and/or in thefirst sequence of color images; predicts a height of the user based on aheight of the lower leg feature above the platform (e.g., a distancefrom a center of the knee to a top position of the platform below); andthen calculates a second path that extends between the end of the firstpath and a target distance (e.g., 20 centimeters) above the predictedheight of the user. Upon completion of the first, predefined path, thescanning system can transition to: driving the depth sensor along thesecond path in Block S120; and recording a second sequence of depthimages and a second sequence of color images along the second path inBlock S130.

Therefore, in this implementation, the scanning system can sweep thesensor block upward from proximal the user's feet, estimate the user'sheight based on data collected early in this scan cycle, and then modifythe remainder of the path of the sensor block in real-time based on theuser's predicted height—such as by refining or regenerating a sequenceof waypoints for this scan cycle—such that top of the user's head iscaptured during this scan cycle while also minimizing a total durationof the scan cycle and a total number of depth images and color imagescaptured during this scan cycle. Thus, in this implementation, thescanning system can modify the path of the sensor block in order tomatch the length and height of the path of the sensor block to theuser's anatomy, thereby eliminating capture positions in which depthimages and color images above the user would otherwise have beenrecorded during the scan cycle, improving convenience of the scan cyclefor the user, necessitating less computational load to transform thesescan data into a 3D model, reducing bandwidth requirements foroffloading these scan data from the scanning system to the remotecomputer system, and necessitating less data storage for the 3D modeland related scan data.

Alternatively, a user portal within the native application or webbrowser executing on the user's local computing device can prompt theuser to enter her height, and the computing device, the remote computersystem, or the scanning system can implement similar methods andtechniques to automatically adjust the path of the sensor block for thefirst scan cycle based on the height entered manually by the user.

Furthermore, the scanning system, computing device, or remote computersystem can store this path customized for the user for the firstcomputer system, such as in the form of a sequence of waypoints; thescanning system can later access and execute this path during a nextscan cycle with the user, as described below.

4.4 Adaptive Scan Parameters: User Weight

In a similar variation, the scanning system can adjust the path of thesensor block during the first scan cycle based on the user's predictedanatomy.

In one implementation, the scanning system samples the load cell underthe platform and records a weight of the user based on an output of theload cell. The scanning system then predicts a maximum width of the userbased on the user's weight. For example, the scanning system can: accessor estimate a height of the user, as described above; and enter theweight of the user and the height of the user in a human anatomy modelto estimate a geometry or maximum width of the user. The scanning systemcan then set a radial offset of the sensor block from the vertical axisof the platform proportional to the predicted maximum width of the user.In particular, the scanning system can: predict the maximum width of theuser proportional to the user's weight and inversely proportional to theuser's height; set a target radial distance of the sensor block offsetradially outwardly from the maximum predicted width of the user by athreshold distance; and then adjust a length of the horizontal segmentof the boom—via the actuator system—to this target radial distancebefore or during the first scan cycle. For example, the scanning systemcan set the target radial distance of the sensor block offset radiallyoutwardly from the maximum predicted width of the user by a thresholddistance of 25 centimeters such that the maximum width of the user(e.g., the user's shoulders, the user's lower torso) is predicted tofill 90% of the width of the field of view of the depth sensor and colorcamera at one capture position during this scan cycle.

The scanning system can then adjust waypoints, sensor block speed, orsampling frequency of the depth sensor and the color camera in order toachieve a minimum overlap between adjacent depth images and betweenadjacent color images during this scan cycle, as described above.

5. User Movement

In another variation, the scanning system can detect motion of the userduring the first scan cycle and selectively pause the scan cycle, repeatsegments of the scan cycle, or serve motion-related prompts to the userresponsive to such detected motion. In particular, motion of the userduring the first scan cycle may not be repeated or reproducible during alater scan cycle. Therefore, while the remote computer system maycalculate transforms that align scan depth images from this first scancycle in which the user exhibits some motion and then generate a first3D model of the user based on these transforms, this first set oftransforms generated according to scan data from the first scan cyclemay greater yield error for initial alignment of depth images recordedduring a second, later scan cycle due to differences in motion of theuser between the first and second scan cycles. Furthermore, excessivemotion of the user during the first scan cycle may introduce errorsufficient to prevent the remote computer system from converging on asolution to compile this first sequence of depth images and thus preventthe remote computer system from generating a 3D model of the user fromthese scan data. The scanning system can therefore track andautomatically respond to user motion during the first scan cycle.

5.1 User Movement: Weight Shift

In one implementation, the scanning system samples the load cell andpredicts user motion—while occupying the platform above—proportional tomagnitude of changes in load detected by the load cell. In thisimplementation, if the scanning system thus detects an absolute orrelative change in load on the platform that exceeds a high thresholdchange—which may correspond to a significant change in position or poseof the user—the scanning system can automatically restart the scancycle. Similarly, if the scanning system detects an absolute or relativechange in load on the platform that falls between a moderate thresholdand the high threshold, the scanning system can pause the scan cycle andthen resume the scan cycle once load on the platform returns to asteady-state. Furthermore, if the scanning system detects an absolute orrelative change in load on the platform that falls between the thresholdand the low threshold, the scanning system can: continue to execute thescan cycle; but output an audible tone to prompt the user to remainstill or interface with the local computing device to serve anotification to remain still to the user.

5.2 User Movement: Optical Flow

In another implementation, the scanning system can implement opticalflow techniques to detect motion of objects (i.e., the user) in thesequence of color images recorded by the color camera during the firstscan cycle. For example, as the scanning system sweeps the sensor blockaround the user, optical flow from consecutive color images recorded bythe color camera may be a function of (e.g., proportional to) known,controlled changes in the azimuth, altitude, and pitch positions of thecolor camera between these consecutive color images if the user issubstantially static (i.e., not moving). For example, as the scanningsystem drives the color camera through a sequence of azimuth, altitude,and pitch positions at consistent speed during this scan cycle, opticalflow may be null or very low from between consecutive color imagesrecorded by the color camera during this scan cycle if the user is notmoving. However, optical flow between consecutive color images mayincrease as a function of increasing magnitude and/or rate of movementsby the user.

Therefore, in this implementation, the scanning system can: detect aregion in a color image depicting a portion of the user (e.g., byscanning laterally from a center of the color image to first edges tothe left and right of the center column of pixels in the color image);crop the color image around the region depicting the user; repeat thisprocess for each subsequent color image recorded by the scanning systemto generate a live sequence of cropped color images depicting portionsof the user; and implement optical flow techniques to characterize usermotion based on changes between these cropped color images in (near)real-time. If the motion of the user exceeds a threshold, the scanningsystem can restart the scan cycle, pause the scan cycle, and/or promptthe user to remain still during the remainder of the computer system,such as described above.

However, the scanning system can implement any other method or techniqueto detect and respond to user motion during the first scan cycle.Furthermore, the local computing device can access scan data recorded bythe scanning system and execute similar methods and techniques—ratherthan the scanning system—to respond to user motion in (near) real-timebased on these scan data.

5.3 User Movement: Preemptive Prompts

In this variation, the scanning system (or the local computing device)can also selectively serve preemptive prompts to remain still to theuser as the scanning system prepares to scan high-interest regions ofthe user's body during the first scan cycle, such as the user's face andbuttocks. By thus selectively prompting the user to remain more stillduring these select periods of the scan cycle, the scanning system cancollect scan data than may enable the remote computer system to generatea 3D color model that depicts these areas of interest on the user withgreater spatial accuracy and smoother, more accurate transitions betweencolor patches.

In one example, the scanning system (or the local computing device, theremote computer system) can: implement computer vision techniques inreal-time to detect regions of the user's body in recent color imagesrecorded by the color camera; and then output an audible tone to promptthe user to remain still when the scanning system determines that thesensor block is facing the user's upper thigh—and therefore approachingthe user's buttocks. The scanning system can again output an audibletone to prompt the user to remain still with her eyes open when thescanning system determines that the sensor block is facing the user'supper torso or neck—and therefore approaching the user's face. Inanother example, the scanning system can predict the proximity of thesensor block to areas of interest on the user based on the position ofthe sensor block and a predicted geometry of the user—such as based onthe user's height, as described.

In this variation, the local computing device can additionally oralternatively output audible prompts or render textual notifications—foruser stillness—synchronized to the position of the sensor block relativeto areas of interest on the user during this scan cycle. However, thescanning system, the local computing device, and/or the remote computersystem can implement any other method or technique to selectively promptthe user to remain still during the first scan cycle.

6. Data Offload

During the scan cycle, the scanning system can automatically uploaddepth images to the remote computer system in real-time as the depthimages are recorded, such as directly via a local wireless area networkor through the user's local computing device via a local ad hoc wirelessnetwork. Alternatively, the scanning system can offload these depthimages to the remote computer system for processing en masse uponconclusion of the first scan cycle.

Furthermore, the scanning system can upload depth images to the remotecomputer system first, followed by color images from the scan cycle.Upon receipt, the remote computer system can process these depth imagesfirst to construct a 3D model of the user's body in Blocks S140 and S150while the scanning system uploads corresponding color images. Uponcompletion of the 3D model of the user, the remote computer system canextract color patches for the 3D model from these color images, therebylimiting delay between recordation of these scan data, generation of the3D model of the user, colorization of the 3D model, and presentation ofa 3D color model to the user at the user's local computing device aftercompletion of the scan cycle.

However, the scanning system can return these depth images and colorimages to the remote computer system in any other way and according toany other schema during or upon conclusion of the scan cycle.

7. Reconstruction into 3D Model

Blocks S140 and S150 of the method S100 recite calculating a first setof transforms that align the first sequence of depth images andcompiling the first set of depth images according to the first set oftransforms to generate a first 3D model of the user for the first time,respectively. Generally, in Blocks S140 and S150, the remote computersystem process depth images recorded by the scanning system during thefirst scan cycle; to converge on a set of transforms that align thesedepth images with minimum spatial error between surfaces represented bysurfaces in these depth images; and to leverage this set of transformsto compile these depth images into a 3D model of the user, such as inthe form of a 3D mesh that can then be colorized to form ahyper-realistic, color-true, texture true representation of the user, asshown in FIG. 8.

In one implementation, the remote computer system: projects the firstsequence of depth images recorded during the first scan cycle into avirtual 3D space to form a first set of point clouds; and coarselyaligns these point clouds based on actual or target azimuthal, altitude,and pitch positions of the sensor block (or the depth sensor morespecifically) when each of these depth images was recorded. The remotecomputer system can then implement iterative closest point (or “ICP”)techniques to: subsample points in these point clouds; characterizeerror between selected points in these point clouds; and shift relativepositions of vertically-overlapping and horizontally-overlapping pointclouds in the virtual 3D space to minimize error between these points.The remote computer system can then store positions of these pointclouds that minimize error between points in the form of a first set oftransforms. The computer system can then apply the first set oftransforms to corresponding point clouds to generate a composite 3Dpoint cloud in the virtual 3D space.

The remote computer system can then: implement smooth signed distance,fast-four transform, or Poisson surface reconstruction techniques totransform the composite 3D point cloud into a mesh representing thesurface of the user; and store this mesh as a (colorless, pre-color) 3Dmodel of the user in Block S150.

7.1 Variation: Photogrammetry

Alternately, the remote computer system can implement photogrammetry orstructure from motion techniques to compile the first sequence of 2Dcolor images recorded during the first scan cycle into a 3D point cloudbased on known changes in position, changes in orientation, linearacceleration, and/or angular velocity of the color camera betweenrecordation of these color images. In this implementation, once theremote computer system has generated a 3D point cloud from these colordata, the remote computer system can: calculate a set of transforms thatminimizes spatial error between points in the first sequence of depthimages and surfaces defined in the color-image-derived 3D point cloud;leverage this set of transforms to compile these depth images into adepth-derived 3D point cloud of the user; and then calculate a surfacemesh representation of the user from this depth-derived 3D point cloud.The remote computer system can later leverage this set of transforms tomove between the domain of the color camera and the domain of the depthsensor for scan data collected both during this first scan cycle andduring later scan cycles.

Alternatively, the scanning system can record color images exclusivelyduring the first scan cycle. The remote computer system can then:implement photogrammetry or similar techniques to construct a 3D pointcloud of the user; generate and store a set of transforms that definepositions of these color images that minimize error between adjacentcolor images; and calculate a surface mesh representation of the userfrom this color-derived 3D point cloud. Later, the local computingdevice can leverage these transforms and (basic, lower-cost)photogrammetry techniques to construct a second color-derived 3D pointcloud and a second surface mesh representation of the user from thissecond sequence of color images recorded by the scanning system during anext scan cycle.

In another variation, the scanning system implements photometrictechniques to illuminate the user with artificial light (e.g., with anLED integrated into the sensor block) and to record multiple colorimages of the user with different lighting patterns per waypoint alongthe scan path during a scan cycle. The scanning system (or the remotecomputer system) can then compile a set of color images recorded at eachwaypoint to 3D information of the user from the perspective of thecamera at this waypoint, such as including high-resolution textureinformation. The scanning system (or the remote computer system) canrepeat this process for color images recorded at each other waypoint andcompile the resulting 3D information into a 3D (texturized)representation of the user.

However, the remote computer system can implement any other method ortechnique to generate a 3D model of the user based on depth imagesand/or color images recorded during the first scan cycle.

8. Reconstructing Color

One variation of the method S100 shown in FIGS. 1A and 8 includes BlocksS110 and S172, which recite: extracting a first set of color patches,from the first sequence of color images, corresponding to a set ofdiscrete regions on a surface of the first 3D model of the user; andprojecting the first set of color patches onto the surface of the first3D model to generate a first 3D color model of the user. Generally, inBlocks S110 and S172, the remote computer system can extract colorpatches from (a small subset of) the first sequence of color imagesrecorded during the first scan cycle; the remote computer system or thelocal computing device can then render these color patches on thesurface of the 3D model (e.g., the surface of low-density mesh of theuser-facing model described below) to generate a realistic, color-true3D color model of the user. The local computing device can then displaythis 3D color model for the user.

In one implementation, the remote computer system identifies a set ofdiscrete regions on the surface of the 3D model, wherein each region inthis set approximates a plane (e.g., contains points or defines meshnodes that fall near a plane) and defines a contiguous surface exceedinga minimum surface area (e.g., 1,000 square millimeters). For each regionin this set of discrete regions, the remote computer system can:identify a particular color image—in the first sequence of color imagesrecorded during the first scan cycle—depicting an area on the user thatcorresponds to this region on the surface of the 3D model; project aboundary of this region onto the particular color image; crop the colorimage around this projected boundary to define a color patch for thisregion on the surface of the 3D model; write a coordinate of thisregion—within a coordinate system defined in the first 3D model—to thecolor patch; and store this color patch in a first set of color patchesfor the first scan cycle. The remote computer system can repeat thisprocess for each other region thus defined on the surface of the 3Dmodel. Later, the remote computer system or the local computing devicecan: access this first set of color patches and the corresponding first3D model for the first scan cycle; and project the first set of colorpatches onto the surface of the first 3D model based on coordinatesassigned to these color patches and the coordinate system of the first3D model to render a 3D color model depicting the user during the firstscan cycle. The local computing device can then display this 3D colormodel for the user.

8.1 Region Selection

In this implementation, the remote computer system can select regions onthe 3D surface for allocation of a 2D color patch based on planarity ofthese regions, as shown in FIG. 8. For example, after generating the 3Dmodel, the remote computer system can implement plane detectedtechniques to scan the surface of the 3D model for regions approximatingplanes, such as contiguous regions: containing points or definingsurface profiles that fall within a threshold distance (e.g., +/−10millimeters) of a plane; and defining contiguous areas greater than aminimum area (e.g., 1,000 square millimeters).

Alternately, the computer system can: prioritize larger regions oversmall regions in order to minimize seams between corresponding colorpatches rendered on the 3D model (which may minimize quantity of colorpatches for the 3D model and limit graphics processing time to renderthe resulting 3D color model); prioritize regions exhibiting less erroror deviation from best-fit planes (which may minimize error resultingfrom projection of 2D color patches onto 3D surfaces in the 3D model);and then calculate a set of regions of that balance maximum region sizeand minimum deviation from planarity.

8.3 Preset Seam Locations

In one variation, the remote computer system implements or approximatespreset seam locations between discrete regions isolated within the 3Dmodel, such as: vertical seams extending from waistline to armpit alongleft and right flanks of the 3D model; a lateral seam along thewaistline or base of the torso in the 3D model; seams extending fromjawline to behind the ear on each side of the head in the 3D model, etc.

In one implementation, the remote computer system: accesses a generichuman body model defining a preset color patch boundary along a flank ofthe generic human body model; deforms the generic human body model intoalignment with the first 3D model; transfers the preset color patchboundary from the generic human body model onto the first 3D model; andthen implements methods and techniques described above to define aboundary between two adjacent discrete regions proximal this presetcolor patch boundary in the first 3D model. The remote computer systemcan thus inject one or many preset color patch boundaries from thegeneric human body model into the 3D model and then segment the 3D modelinto multiple discrete regions based on these preset color patchboundaries. For example, the remote computer system can define a set ofregions—across the entire surface of the 3D model—that minimizes a costbetween largest regions, most-planar regions, and proximity of regionboundaries to preset color patch boundaries. Alternatively, the remotecomputer system can snap region boundaries directly onto closest presetcolor patch boundaries.

However, the remote computer system can segment the surface of the 3Dmodel in any other way and according to any other parameters.

8.4 3D Space to Color Space

The remote computer system can then extract a color patch from a 2Dcolor model for each discrete region thus defined on the surface of the3D model.

For example, for each region thus isolated on the surface of the 3Dmodel, the remote computer system can: calculate a centroid of theregion; calculate a target sensor block position—at the scanningsystem—that locates the focal axis of the color camera at a point on theuser corresponding to the location of this centroid on the 3D model; andthen retrieve a particular color image—from the first sequence of colorimages—recorded by the color camera when the sensor block occupied anactual position nearest this target position during the first scancycle. The remote computer system can then locate this particular 2Dcolor image with the 3D model based on this actual position of thesensor block when the particular color image was recorded and a fixed,known offset between the color camera and depth sensor. For example, theremote computer system can: retrieve a particular transform that aligneda concurrent depth image—recorded by the depth sensor when the sensorblock occupied the same target position during the first scan cycle—withother depth images in the 3D model; apply the particular transform tothe particular color image to locate the color image relative to the 3Dmodel; and adjust (or “correct” the position of the particular colorimage relative to the 3D model based on a known offset between the colorcamera and the depth sensor, such as defined in a static, stored sensortransform. The remote computer system can then: project a boundary ofthe particular region on the 3D model onto the particular color image;crop the color image around the 2D boundary; and store this cropped areaof the color image as a color patch for this region on the surface ofthe 3D model. The remote computer system can repeat this process foreach other region on the surface of the 3D model to generate a completeset of patches for the 3D model.

However, the remote computer system can implement any other method ortechnique to generate a color patch for a region on the surface of the3D model.

9. Texture

In one variation as shown in FIG. 8, the remote computer system furtheraugments the first 3D model of the user with texture information inBlock S174.

9.1 2D Bump Map

In one implementation shown in FIG. 6, the remote computer system scansa color patch—in the first set of color patches for the first 3Dmodel—for a field depicting a homogeneous color pattern, such as a fieldcontaining pixels of substantially uniform distribution of similar colorblobs, depicting similar edge features, and/or depicting substantiallyuniform distribution of these similar edge features. The remote computersystem can then implement deep learning, artificial intelligence,template matching or other computer vision techniques to predict atexture type depicted in the field based on this homogeneous colorpattern, such as by matching features extracted from the field in thecolor patch to stored template features for one of: loose skin; tight orsmooth skin; wrinkle skin; lip tissue; eyes; hair (and different typesof hair, such as straight, wavy, curly, and kinky hair types);fingernails; acne; acne scarring; cellulitis, and clothing. Accordingly,the remote computer system can retrieve a texture model—from a set ofpredefined texture models characterizing various texture types—for theidentified texture type depicted in the field. For example, the texturemodel can include a 2D bump map, and the remote computer system cancombine this 2D bump map with the corresponding field in the 2D colorpatch, such as by projecting this 2D bump map onto the correspondingfield or by rendering this 2D bump map within this field in the 2D colorpatch. In this implementation, the remote computer system can repeatthis process to inject texture data into other fields depicting othertexture types in this color patch, and the remote computer system canrepeat this process for each other color patch associated with the first3D model. Later, the local computing device (or the remote computersystem) can render these texturized color patches over their assignedregions on the surface of the 3D model of the user to generate the 3Dcolor, texturized model of the user.

Alternatively, the remote computer system can: generate a separate 2Dtexture patch spanning the boundary of the field in the 2D color patch;inject the 2D bump map from the texture module into the 2D texturepatch. In this implementation, the remote computer system can repeatthis process to generate texture patches for fields depicting othertexture types in this color patch and for such texture fields in eachother color patch associated with the first 3D model. Later, the localcomputing device (or the remote computer system) can render both thesecolor patches and texture patches over their assigned regions on thesurface of the 3D model of the user to generate the 3D color, texturizedmodel of the user.

9.2 3D Texture Function

In another implementation, the remote computer system implements methodsand techniques described above: to scan a color patch—in the first setof color patches for the first 3D model—for a field depicting anhomogeneous color pattern and to predict a texture type of the fieldbased on the homogeneous color pattern. In this implementation, theremote computer system then: retrieves a texture model—for this texturetype of the field in the form of a mathematical function (e.g., amathematical function characteristic of a 2D bump map for this sametexture); implements methods and techniques described below to locatethe color patch on the corresponding region on the surface of the 3Dmodel; transfers a boundary around the field in the projected colorpatch onto the surface of the 3D model to define a texture boundary forthis texture type; and then deforms a region on the surface of the first3D model—contained within this texture boundary—according to themathematical function. For example, the remote computer system cangenerate an isosurface based on this mathematical function and merge (or“fuse”) this isosurface with the corresponding region on the surface ofthe 3D model.

Therefore, in this implementation, the remote computer system canlocally deform the surface of the 3D model (e.g., the low-density 3Dmesh described below) to reflect the texture detected in thecorresponding area on the color patch. The remote computer system canrepeat this process for the same and other textures detected in otherareas through the first set of color patches.

9.3 Texture Magnitude

In this variation, the remote computer system can also extract amagnitude of a texture from a color patch. For example, the remotecomputer system can adjust a texture model projected onto a field in apatch or incorporated into the surface of the 3D model to reflect:density and depth of wrinkles; thickness of hair; distribution ofcellulitis; and/or smoothness of skin.

In one implementation, after detecting a primary texture type within afield within a color patch, the remote controller can: extract anintensity gradient of the texture type from the field in the colorpatch; retrieve a texture model for this texture type in the form of abump map; and then project this bump map across the field in the colorpatch as a function of the intensity gradient for the texture type inthe field, such as by setting an opacity or scale of features in theprojected bump map proportional to intensity values stored in theintensity gradient. In another example, the remote computer system can:access a database of texture models, including texture models depictingthe same texture type at different intensities or magnitudes; retrieve aparticular bump map corresponding to the texture type detected within afield of a color patch and depicting this texture type at a magnitudenearest the magnitude detected in the field; and then inject thistexture model across the corresponding field in the color patch.

Alternately, in the implementation described above in which the remotecomputer system adjusts the surface of the 3D model to reflect a texturetype detected in a corresponding field in a color patch, the remotecomputer system can: extract an intensity gradient of the texture typein the field in the color patch; generate an isosurface based on themathematical function with coefficients of the mathematical functionadjusted based on values in the intensity gradient such that theisosurface reflects spatial intensity of this texture type on the user'sbody; and then fuse this isosurface with the corresponding region on thesurface of the 3D model.

9.4 Selective Texturing

In the foregoing implementations, the remote computer system can alsoselectively texturize regions on the surface of the 3D model in order toreduce processing and rendering time for texture on the 3D model whileensuring that texture is accurately detected in common areas of intereston the 3D model, such as the user's face, stomach, and buttocks.

10. Variation: Backend and User-Facing 3D Models

In one variation shown in FIGS. 8 and 9 , the remote computer system canimplement smooth signed distance, fast-four transform, or Poissonsurface reconstruction techniques: to generate a high-density 3D mesh inBlock S160; and to generate a low-density 3D mesh in Block S150, whichmay lose spatial resolution over the high-density 3D mesh but requireless graphics load or processing to render than the high-density 3Dmesh. The remote computer system can then generate a set of colorpatches and texture map for the low-density 3D mesh, as described above.The user's local computing device can access these color patches,texture map, and low-density 3D mesh, render the color patches andtexture map over the surface defined by the low-density 3D mesh togenerate a user-facing 3D color model of the user, display thisuser-facing 3D color model for the user, and receive dimension queriesover the user-facing 3D color model from the user in Block S180.Separately, the remote computer system can: store the high-density 3Dmesh as a backend 3D model of the user; project dimension queriesreceived on the user-facing 3D color model onto the backend 3D model;extract dimensional values for these queries from this higher-accuracybackend 3D model in Block S182; and then return these dimensional valuesto the local computing device for presentation to the user. Thus, bygenerating a user-facing 3D model containing a lower-density mesh and abackend 3D model containing a higher-density mesh in Blocks S150 andS160, the remote computer system can enable a 3D color model of the userto be rendered relatively quickly at a local computing device (orpre-rendered relatively quickly by the remote computer system) and thusreduce user wait time after the first scan cycle while also preservinghigh spatial accuracy in a representation of the user thus generatedfrom scan data collected during the first scan cycle.

Therefore, in this variation, the remote computer system can: generate auser-facing 3D model—including a lower-density mesh paired with colorpatches and/or texture data—for presentation to the user (or affiliatedentity, such as an athletic coach, trainer, therapist, or physician) inBlocks S150 and S152; and generate a backend 3D model—including ahigher-density mesh and absent color and texture data—for extraction ofhigh-precision measurement based on the same scan data collected duringthe first scan cycle.

The remote computer system can therefore store the user-facing 3D modelpaired with the concurrent backend 3D model.

10.1 Dimension Extraction from Backend 3D Model

The remote computer system can also: serve the user-facing 3D model to alocal computing device for rendering and display for the user (or anaffiliated entity); receive the corresponding backend 3D model when theuser-facing 3D model is open at the local computing device; and thenextract measurements from the backend 3D model based on selection madeon the user-facing 3D model.

In one implementation shown in FIG. 9 , the local computing device:accesses the first user-facing 3D model—including the first low-densitymesh and the first set of texturized color patches—once generated by theremote computer system following completion of the first scan cycle;renders the first set of texturized color patches on the surface of thelow-density mesh to generate a first 3D color model of the user; andthen displays the first 3D color model on a connected or integrateddisplay. The local computing device can then receive selection of adimension on the first 3D color model, such as: a width of the waist; acircumference of the waist; a width of the shoulders; a circumference ofa neck; a total height; an inseam length; a foot length; or a bicepcircumference; etc. For example, the local computing device canhighlight these common corporeal measurements—such as by deforming ageneric common human dimension model onto the 3D color model,transferring common corporeal measurements from the generic common humandimension model onto the 3D color model, and prompting the user toselect from these common corporeal measurements—and then recordselection of a common corporeal measurement from this set.Alternatively, the local computing device can record selection of twopixels or mesh nodes or selection of a surface profile on the 3D colormodel. The local computing device can return this dimension selection tothe remote computer system, which can then: transfer this dimensionselection (e.g., the common corporeal measurement, the selected pixelsor nodes, or the surface profile) onto the backend model; extract avalue of this dimension—thus projected from the user-facing 3D colormodel onto the backend 3D model—such as in the form of a distancebetween two points, a length, an area, a volume, or a surface profile;and then return this extracted value to the local computing device forpresentation to the user (e.g., in near real-time).

Therefore, the remote computer system can store both the user-facing 3Dmodel paired with the concurrent backend 3D model, receive measurementqueries entered at the user-facing 3D model, transfer measurementqueries onto the backend 3D model, extract values for these measurementqueries from the backend 3D model, and return these values to the localcomputing device for presentation with the user-facing 3D model. Thus,the remote computer system and the local computing device can leveragethe user-facing and backend 3D model pair to communicate ahyper-realistic, color- and texture-true representation of the user'sbody to the user (or affiliate) while also preserving access to ahigh-precision volumetric representation of the user's body, whichtogether may enable the user to rapidly access both a lower-bandwidth,lighter-weight visual representation of the user and high-precisionmetrics related to the user's body.

11. Preparation for Future Scan

The remote computer system (and/or the scanning system, the localdevice) can aggregate scan cycle, data capture, data processing, anduser characteristics and parameters and then store these characteristicsand parameters for implementation during execution of a next scan andprocessing of resulting scan data into a next 3D color model of theuser.

For example and as shown in FIG. 8, the remote computer system cangenerate a scan file containing: data capture positions (or “waypoints”)of the sensor block in the scanning system at which each depth imagecontained in the first 3D model of the user and at which each colorimage containing a color patch in the first set of color patches wasrecorded during the first scan cycle; a first set of transforms forcompiling a subset of depth images in the first sequence of depth imagesinto a user-facing 3D model with low mesh density; (a second set oftransforms for compiling the same or other subset of depth images in thefirst sequence of depth images into a backend 3D model with high meshdensity;) color image capture positions, crop areas, and coordinates forcolor patches in the first set of color patches; and locations, texturetypes, and texture magnitudes for textures in fields in color patches oron regions of the 3D color model. The remote computer system can thenassociate this scan file with the user, such as with the user's profile(e.g., a unique identifier assigned to the user), the measured weight ofthe user during the first scan cycle, the user's lower leg featuresdetected by the scanning system during the first scan cycle, or theuser's facial features detected by the scanning system during the firstscan cycle.

However, the remote computer system (or the scanning system, the localcomputing device) can aggregate and store any other data related to theuser, the first scan cycle, and/or the first 3D color model based ondata collected during the first scan cycle.

12. Next Scan and Next 3D Model

During a next scan cycle, the scanning system and the local computingdevice (and/or the remote computer system) can leverage these storeddata from the first scan cycle to simplify the next scan cycle and/orgenerate a second 3D model of the user in less time and/or with lesscomputational load.

12.1 Scan Parameters

In one implementation, the user logs in to the scanning system or to heruser profile at a user portal within a native application or web browserexecuting on her computing device in order to initiate a new scan cycle(e.g., a second scan cycle) at the scanning system. The scanning systemor the computing device can then retrieve a scan file—associated withthe user's profile—generated previously based on data collected duringthe user's first scan cycle at the scanning system (or at another,similar scanning system). The scanning system can extract a sequence ofdata capture positions (or “waypoints”) from the scan file, initiate anext scan cycle with the user, drive the sensor block through thissequence of data capture positions, and trigger the depth sensor and thecolor camera to capture depth images and color images at each of thesedata capture positions.

In another example, during the first scan cycle, the scanning systemcan: record a first weight of a load (i.e., the user) on the platform;and record azimuth, altitude, and pitch positions occupied by the depthsensor during recordation of each depth image in the first sequence ofdepth images. The remote computer system can then store these waypointsin the scan file for the first scan cycle and link this scan file to theweight of the user. Later, when the same user steps onto the platform toinitiate a second scan cycle, the scanning system can: record a secondweight of a load on the platform; query a local or remote database forhistorical weights recorded during past scan cycles at the scanningsystem; isolate a particular historical weight that differs from thesecond weight by less than a threshold difference; retrieve a scan fileassociated with the particular historical weight; extract a firstsequence of capture positions stored in the scan file; and then recorddepth images and color images with the sensor block occupying all or asubset of these capture positions in Blocks S112, S122, and S132.

However, the scanning system can identify the user based on any otheridentifying feature detected before or during the second scan cycle(e.g., lower leg features, predicted height). The scanning system canthen retrieve a scan file for the user or access a sequence of capturepositions from the first scan cycle to repeat during this next scancycle based on the user's identity.

12.2 Reduced Data Capture

In one implementation, the remote computer system populates the scanfile with a subset of capture positions executed during the first scancycle such that a second sequence of depth images and color imagesrecorded by the scanning system during later scan cycles exhibit lessoverlap (e.g., 5% overlap rather than 50% overlap) but still containsufficient information to generate additional 3D color models (andbackend 3D models) of the same or similar spatial, color, and textureaccuracy and with similar mesh densities. In particular, the scanningsystem can record depth images and color images at a high density ofcapture positions during the first scan cycle to enable the remotecomputer system to reconstruct a first high-definition 3D color model ofthe user without previous understanding of the dynamics of the scanningsystem or the anatomy of the user. Once the remote computer system hasgenerated this first 3D color model of the user, the remote computersystem can filter the sequence of capture positions from the first scancycle down to a subset of capture positions that, when executed by thescanning system, enable capture of a smaller number of depth images anda smaller number of color images that can be combined according totransforms and color patch definitions from the first scan cycle tocomplete a new 3D model of the user.

For example, the remote computer system can aggregate an initial list ofcapture positions corresponding to color images containing every colorpatch in the first set of color patches. Then, given a depth imagerecorded at every capture position in the initial list of capturepositions, the remote computer system can filter first sequence ofcapture positions from the first scan cycle down to a second sequence ofcapture positions that include: the initial list of capture positions;and additional capture positions between the initial list of capturepositions that yield a second target overlap (e.g.,5%) ofpoints—depicting the user's body—in adjacent depth images captured atthese capture positions given known geometries of regions of the user inthe field of view of the depth sensor at these capture positions.

In particular, the sensor block may fall closer to the body of aheavyset user with a large torso circumference such that a smallproportion of the circumference of this user's torso falls within thefield of view of the depth image at each capture position. Therefore,the remote computer system can define a higher density of capture pointsaround this user's torso in this second sequence of capture positions inorder to achieve the target overlap between adjacent depth images duringa next scan cycle. Conversely, the sensor block may fall further fromthe body of a lean user with a small torso circumference such that alarge proportion of the circumference of this user's torso falls withinthe field of view of the depth image at each capture position;therefore, the remote computer system can define a lower density ofcapture points around this user's torso in this second sequence ofcapture positions in order to achieve the target overlap betweenadjacent depth images during a next scan cycle. The remote computersystem can similarly define a smaller density of capture positionsaround the user's feet and head and a larger density of capturepositions around the user's torso for both lean and heavyset users.Therefore, the remote computer system can leverage the first 3D model ofthe user, which depicts the actual anatomy (or “shape”) of the user, toselect a variable density of capture positions from the first scan cycleto achieve this second target overlap between points—in adjacent depthimages—depicting the surface of the user's body.

The remote computer system can then store this second sequence of scanpositions in the scan file for the user. During the second scan cycle,the scanning system can thus access this second sequence of capturepositions, drive the sensor block through this second sequence ofcapture positions, and record a depth image and color image pair at eachof these second sequence of capture positions. Because this secondsequence of capture positions contains a lower number and lower densityof capture positions than the first sequence of capture positionscompleted in the first scan cycle, the scanning system can execute thesecond scan cycle at greater speed—and therefore in less time—than thefirst scan cycle. For example, the scanning system can implementclosed-loop controls: to sweep the sensor block along a helical pathdefined by the first sequence of capture positions at a first speed(e.g., 0.2 m/s) during the first scan cycle; and to sweep the sensorblock along the same or similar helical path defined by the secondsequence of capture positions at a second speed (e.g., 0.5 m/s) duringthe second scan cycle.

Additionally or alternatively, by capturing depth images and colorimages at fewer, more targeted capture positions, the scanning systemcan generate less raw data during the second scan cycle, therebyrequiring transfer of less data to the remote computer system or localcomputing device for processing, reducing bandwidth requirements for alocal wireless network connected to the scanning system, reducing dataoffload time, and reducing processing time for transforming these scandata into a second 3D color model of the user.

12. 3Data Filtering

Alternatively, during the second scan cycle, the scanning system canexecute the same path and capture depth images and color images at thesame set of capture positions as the first scan cycle. For example, thescanning system can record the actual path traversed by the sensor blockduring the scan cycle based on position data collected from sensors(e.g., optical encoders) coupled to the actuator system, boom segments,and the sensor block and based on linear acceleration and angularvelocity data collected from the IMU during the first scan. The scanningsystem can then: implement closed-loop controls to reproduce thesepositions, linear accelerations, and angular velocities of the actuatorsystem, boom segments, and sensor block; and capture depth image andcolor image pairs at the same capture positions during the second scancycle.

The scanning system can then access the scan file for the user andoffload (e.g., to the local computing device or to the remote computersystem) only depth images and color images—recorded during the secondscan cycle—tagged with capture positions specified in the user's scanfile.

Thus, in this implementation, the scanning system can repeat the pathand capture positions of the first scan cycle but selectively offload asubset of depth images exhibiting reduced overlap and color imagesspecifically containing color patches defined in the first 3D colormodel, thereby requiring transfer of less data to the remote computersystem or local computing device for processing, reducing bandwidthrequirements for a local wireless network connected to the scanningsystem, reducing data offload time, and reducing processing time fortransforming these scan data into a second 3D color model of the user.

In this implementation, the scanning system (or the local computingdevice) can also identify the user upon conclusion of the second scancycle, such as by implementing facial recognition techniques to matchfacial features detected in a color image recorded near the end of thesecond scan cycle to facial features depicted in the first 3D colormodel of the user or in a color image recorded during the first scancycle with the user in order to unique identify the user. The scanningsystem can then implement the foregoing methods and techniques toretrieve the scan file associated with this user and to selectivelyoffload depth images and color images specified in the scan file to thelocal computing device or to the remote computer system for processing.In a similar example, upon conclusion of the second scan, the scanningsystem can transmit a color image predicted to depicted the face of anoccupant standing on the platform to the local computing device, such asvia a local or ad hoc wireless network. The local computing device (orthe remote computer system) can then identify the user in this colorimage, retrieve the scan file associated with this user from a remotedata, and query the scanning system for a subset of depth images andcolor images specified in this scan file.

However, in this implementation, the local computing device or theremote computer system can cooperate with the scanning system to accessdepth images and color images recorded at a subset of target capturepositions during the second scan cycle.

12.4 Reduced Processing Complexity

Once the local computing device (or the remote computer system) accessesthe second sequence of depth images and color images from the secondscan cycle, the local computing device can leverage transforms (e.g.,maps, transformation matrices) stored in the user's scan file togenerate an initial alignment of these depth images. More specifically,the local computing device (or the remote computer system) canpre-position this second sequence of depth images based on the first setof transforms stored in the scan file in order to reduce initialcomplexity of aligning these depth images. Once these depth images areprepositioned according to this first set of transforms, the localcomputing device, the local computing device can locally implement ICPtechniques described above to refine these transforms and thus reduceerror between surface profiles represented by groups of points insmaller overlapping regions of these depth images.

Once the local computing device converges on a refined set of transformsthat minimize error between this second set of depth images, the localcomputing device can compile these depth images into a second 3D modelof the user based on this refined set of transforms. The local computingdevice can also update transforms stored in the user's scan file toreflect these refined transforms from the second scan cycle, which mayenable the local computing device to account for anatomical changesoccurring in the user over time when prepositioning depth imagesrecorded during future scan cycles.

The local computing device can also retrieve color patch definitionsfrom the user's scan file and apply these definitions directly to thesecond sequence of color images to generate a set of color patches (ortexturized color patches) for the second 3D model of the user.

For example, the remote computer system can record a first set ofcoordinates, a first set of crop area definitions, and a first subset ofcapture positions of color images corresponding to the first set ofcolor patches for the first 3D model of the user. During the second scancycle, the scanning system can record a second sequence of color imagesat each capture position in this first subset of capture positions. Thelocal computing device can then: extract a second set of color patchesfrom the second sequence of color images based on the first set of croparea definitions; project the second set of color patches onto a surfaceof the second 3D model based on the first set of coordinates to generatea second 3D color model of the user; and then display this second 3Dcolor model for the user.

Alternatively, the local computing device can: calculate a differencebetween the first set of transforms from the first scan cycle and therevised set of transforms from the second scan cycle; shift color patchdefinitions to reflect this difference based on a known offset betweenthe color camera and depth sensor; and then implement these refinedcolor patch definitions to extract color patches from the secondsequence of color images. The local computing device can also update theuser's scan file to reflect these refined color patch definitions.

Furthermore, the local computing device can locally implement objectrecognition, template matching, and/or computer vision techniques todetect fields depicting different textures in these colored patches andto execute processes described above to incorporate 2D features (e.g.,bump maps) depicting these detected textures in the second set of colorpatches or to incorporate 3D features (e.g., isosurfaces) depictingthese detected textures in the second 3D model of the user.

12.5 Motion-Based Correction

In one variation, the scanning system implements methods and techniquesdescribed above to record actual azimuth, altitude, and pitch positionsof the sensor block when depth images and color images were capturedduring the first scan cycle and during the second scan cycle; the remotecomputer system can store these actual position data for the first scancycle into the scan file for the user. Later the local computing devicecan: detect deviations between the actual position of the sensor blockwhen a particular depth image was recorded during the second scan cycleand the actual position of the sensor block when the corresponding depthimage was recorded during the first scan cycle; adjust the transform forthis capture position to compensate for (or “counter”) this deviation;and repeat the process to correct transforms for other depth imagesrecorded during the second scan cycle before prepositioning these depthimages in Block S152. The local computing device can then implementmethods and techniques described above to refine alignment of thissecond sequence of scan cycles.

12.6 Processing Location

Therefore, the scanning system, the local computing device, and theremote computer system can cooperate to configure scanning and modelconstruction parameters for a second scan cycle based on data collectedduring a first scan cycle in order: reduce latency between completion ofthe second scan cycle and generation of a 3D color model of the user;reduce computational load to generate the 3D color mode; reducebandwidth, time, and expense of accessing scan data captured by thescanning system; and/or enable efficient generation of the 3D colormodel with local, lower-cost computation power of a local computingdevice. More specifically, the scanning system, the local computingdevice, and the remote computer system can leverage data generatedduring construction of the first 3D color model of the user to: reducetime to generate a second 3D color model of the user; reduce time toreturn the second 3D color model to the user by reducing or eliminatingtime to upload scan data to the remote computer system and to downloadthe second 3D color model to the local computing device; and reduce costto generate the second 3D model by eliminating cost of uploading scandata and downloading the completed second 3D color model and byleveraging local compute at the local computing device and/or at thescanning system rather than contracting a remote computer system forprocessing of these scan data.

For example, the remote computer system can: access a first sequence ofdepth images and color images recorded by the scanning system during afirst scan cycle; calculate a first set of transforms that align surfaceprofiles represented by points in the first sequence of depth images;extract a set of color patches for the first 3D model from the firstsequence of color images; and store imaging, reconstruction, and colorparameters in a scan file associated with the user. Hours, days, weeks,or months later, the user can initiate a second scan cycle on the same(or similar) scanning system, and the scanning system can record asecond sequence of depth images and color images of the user during thesecond scan cycle. A local computing device (e.g., the user's smartphoneor tablet, rather than the remote computer system) can then: access thissecond sequence of depth images and color images; access a subset of thefirst set of transforms calculated by the remote computer system for thefirst scan cycle; compile the second set of depth images according tothe subset of the first set of transforms to generate a second 3D modelof the user; leverage definitions for the first set of color patches togenerate a second set of color patches for the second 3D model of theuser; render the second set of color patches on the surface of thesecond 3D model; and then display the second 3D color model for theuser, such as in less than one minute from completion of the second scancycle.

Furthermore, the local computing device can generate both the second,user-facing 3D color model that contains a low-density mesh and generatea second backend 3D model that contains a high-density mesh based onscan data collected during the second scan cycle. The local computingdevice can then: display the second, user-facing 3D color model for theuser; store or cache the second backend 3D model in local memory;receive selection of a measurement on the second, user-facing 3D colormodel from the user; query the second backend 3D model—stored in localmemory—for a dimension of this measurement; and then display this valuefor the user. In this implementation, the local computing device canalso upload a copy of the second user-facing 3D color model and/or acopy of the second backend 3D model to the remote computer system or aremote database for longer-term storage.

12.6 Processing Location

In one variation, the scanning system executes an abbreviated scan cycleto capture depth images and/or color images at a subset of positionsabout a target segment of the user's body—such as the user's head, upperarms, waist, or thighs—and to generate a 3D color model of this targetbody segment (i.e., rather than of the user's entire body), which mayreduce duration of the scan cycle and subsequent processing time. Theremote computer system (or the scanning system) can also incorporatethis new 3D color model of the target body segment into a last complete3D color model of the user's body before presenting this hybrid 3D colormodel to the user, thereby enabling the user to view the new 3D colormodel of the target segment of her body in the context of a complete 3Dcolor model of her body.

The systems and methods described herein can be embodied and/orimplemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a user computer or mobile device,wristband, smartphone, or any suitable combination thereof. Othersystems and methods of the embodiment can be embodied and/or implementedat least in part as a machine configured to receive a computer-readablemedium storing computer-readable instructions. The instructions can beexecuted by computer-executable components integrated bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component can bea processor but any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the embodiments of the invention without departing fromthe scope of this invention as defined in the following claims.

I claim:
 1. A method for modeling a human body comprising: initiating afirst scan cycle while a user occupies a scale platform at a first time;during the first scan cycle: driving a depth sensor along a path abovethe platform and about the user; and recording a first sequence of depthimages, via the depth sensor, at each capture position in a firstsequence of capture positions at a first density along the path;calculating a first set of transforms that align the first sequence ofdepth images; compiling the first set of depth images according to thefirst set of transforms to generate a first 3D model of the user for thefirst time; initiating a second scan cycle while the user occupies thescale platform at a second time succeeding the first time; during thesecond scan cycle: driving the depth sensor along the path; andrecording a second sequence of depth images, via the depth sensor, ateach capture position in a second sequence of capture positions at asecond density less than the first density along the path; and compilingthe second set of depth images according to the first set of transformsto generate a second 3D model of the user for the second time.
 2. Themethod of claim 1: wherein driving the depth sensor along the pathduring the first scan cycle comprises sweeping the depth sensor alongthe path at a first speed during the first scan cycle; and whereindriving the depth sensor along the path during the second scan cyclecomprises sweeping the depth sensor along the path at a second speedgreater than the first speed during the second scan cycle.
 3. The methodof claim 1: wherein driving the depth sensor along the path during thefirst scan cycle comprises driving the depth sensor through a firstsequence of waypoints, each waypoint in the first sequence of waypointsdefining a combination of azimuth, altitude, and pitch positions of thedepth sensor corresponding to more than a minimum degree of overlap in afield of view of the depth sensor with adjacent waypoints in the firstsequence of waypoints; wherein recording the first sequence of depthimages comprises recording a depth image at each waypoint in the firstsequence of waypoints to generate the first sequence of depth images;wherein driving the depth sensor along the path during the second scancycle comprises driving the depth sensor through a subset waypoints inthe first sequence of waypoints, each waypoint in the subset ofwaypoints defining a combination of azimuth, altitude, and pitchpositions of the depth sensor corresponding to less than the minimumdegree of overlap in the field of view of the depth sensor with adjacentwaypoints in the subset of waypoints; and wherein recording the secondsequence of depth images comprises recording a depth image at eachwaypoint in the subset of waypoints to generate the second sequence ofdepth images.
 4. The method of claim 1, further comprising: during thefirst scan cycle: recording the first sequence of capture positions,each capture position in the first sequence of capture positionsdefining azimuth, altitude, and pitch positions occupied by the depthsensor during recordation of a depth image in the first sequence ofdepth images; and recording a first weight of a load on the platform;associating the first sequence of capture positions with the firstweight; and during the second scan cycle: recording a second weight of aload on the platform; in response to second weight differing from thefirst weight by less than a threshold difference, retrieving the firstsequence of capture positions; and selecting a subset of capturepositions from the first sequence of capture positions to define thesecond sequence of capture positions.
 5. The method of claim 1, furthercomprising: during the first scan cycle, recording a first sequence ofcolor images, via a color camera coupled to the depth sensor, at eachcapture position in the first sequence of capture positions; extractinga first set of color patches, from the first sequence of color images,corresponding to a set of discrete regions on a surface of the first 3Dmodel of the user; and projecting the first set of color patches ontothe surface of the first 3D model to generate a first 3D color model ofthe user.
 6. The method of claim 5: wherein extracting the first set ofcolor patches from the first sequence of color images comprises:identifying the set of discrete regions on the surface of the first 3Dmodel, each region in the set of discrete regions approximating a planeand defining a contiguous surface exceeding a minimum surface area; foreach region in the set of discrete regions: identifying a color image,in the first sequence of color images, depicting an area on the usercorresponding to the region on the surface of the first 3D model;projecting a boundary of the region onto the color image; cropping thecolor image around the boundary to define a color patch for the region;and writing a coordinate of the region, within a coordinate system inthe first 3D model, to the color patch; and wherein projecting the firstset of color patches onto the surface of the first 3D model to generatethe first 3D color model of the user comprises projecting the first setof color patches onto the surface of the first 3D model based oncoordinates of the first set of color patches and the coordinate systemin the first 3D model.
 7. The method of claim 6, wherein identifying theset of discrete regions on the surface of the first 3D model comprises:accessing a generic human body model defining a preset color patchboundary along a flank of the generic human body model; deforming thegeneric human body model into alignment with the first 3D model;transferring the preset color patch boundary from the generic human bodymodel onto the first 3D model; and defining a boundary between twoadjacent discrete regions, in the set of discrete regions, proximal thepreset color patch boundary in the first 3D model.
 8. The method ofclaim 6, further comprising: recording a first set of coordinates, afirst set of crop area definitions, and a first subset of capturepositions of color images, in the first set of color images,corresponding to the first set of color patches; during the second scancycle, recording a second sequence of color images, via the colorcamera, at each capture position in the first subset of capturepositions; extracting a second set of color patches from the secondsequence of color images based on the first set of crop areadefinitions; and projecting the second set of color patches onto asurface of the second 3D model based on the first set of coordinates togenerate a second 3D color model of the user.
 9. The method of claim 5:wherein compiling the first set of depth images according to the firstset of transforms to generate the first 3D model of the user for thefirst time comprises compiling the first set of depth images into thefirst 3D model comprising a low-density mesh; and further comprising:compiling the first set of depth images to generate a first 3D backendmodel comprising a high-density mesh; at a computing device affiliatedwith the user, displaying the first 3D color model of the user on adisplay of the computing device following conclusion of the first scancycle; at the computing device, receiving selection of a dimension onthe first 3D color model; extracting a value of the dimension projectedfrom the low-density 3D color model onto the high-density 3D model; andat the computing device, displaying the value of the dimension.
 10. Themethod of claim 5, further comprising, for a first color patch in thefirst set of color patches: detecting a field depicting a homogeneouscolor pattern in the first color patch; predicting a texture type of thefield based on the homogeneous color pattern; retrieving a texture modelfor the texture type of the field from a set of predefined texturemodels characterizing a set of texture types, the texture modelcomprising a 2D bump map; and incorporating the texture model into thefield in the first color patch.
 11. The method of claim 10: whereinpredicting the texture type of the field comprises identifying thetexture type in the field selected from the set of texture typescomprising: smooth skin, wrinkled skin, cellulitis, and hair; furthercomprising extracting an intensity gradient of the texture type from thefield in the first color patch; and wherein incorporating the texturemodel into the field in the first color patch comprises projecting thetexture model across the field in the first color patch according to theintensity gradient.
 12. The method of claim 5: further comprising, for afirst color patch in the first set of color patches: detecting a fielddepicting a homogeneous color pattern in the first color patch;predicting a texture type of the field based on the homogeneous colorpattern; retrieving a texture model for the texture type of the fieldfrom a set of predefined texture models characterizing a set of texturetypes, the texture model comprising a mathematical function; anddeforming a first region of the first 3D model, corresponding to thefield in the first color patch, according to the mathematical function.13. The method of claim 1, wherein driving the depth sensor along thepath and recording the first sequence of depth images during the firstsan cycle comprises: driving the depth sensor along a first helicalpath; recording a first subset of depth images, in the first sequence ofdepth images, along the first helical path; detecting a lower legfeature offset above the platform in the first subset of depth images;predicting a height of the user based on a height of the lower legfeature above the platform; calculating a second helical path based onthe height of the user; driving the depth sensor along the secondhelical path; and recording a second subset of depth images, in thefirst sequence of depth images, along the second helical path.
 14. Themethod of claim 1: wherein initiating the first scan cycle comprisinginitiating the first scan cycle at a scanning system comprising theplatform, the depth sensor, and an actuator system configured to sweepthe depth sensor about the path; wherein calculating the first set oftransforms that align the first sequence of depth images comprises, at aremote computer system: accessing the first sequence of depth imagesfrom the scanning system; and calculating the first set of transformsthat align surface profiles represented by points in the first sequenceof depth images; wherein compiling the second set of depth imagesaccording to the first set of transforms to generate the second 3D modelof the user comprises, at a mobile device proximal the scanning system:accessing the second sequence of depth images; accessing a subset of thefirst set of transforms from the remote computer system; and compilingthe second set of depth images according to the subset of the first setof transforms to generate the second 3D model of the user.
 15. A methodfor modeling a human body comprising: initiating a first scan cyclewhile a user occupies a scale platform at a first time; during the firstscan cycle: driving a depth sensor along a path above the platform andabout the user; and recording a first sequence of depth images, via thedepth sensor, corresponding to a first sequence of capture positions ata first density along the path; calculating a first set of transformsthat align the first sequence of depth images; compiling the first setof depth images according to the first set of transforms to generate afirst 3D model of the user for the first time; initiating a second scancycle while the user occupies the scale platform at a second timesucceeding the first time; during the second scan cycle: driving thedepth sensor along the path; and recording a second sequence of depthimages, via the depth sensor, corresponding to a second sequence ofcapture positions at the first density along the path; and compiling asubset of the second set of depth images according to the first set oftransforms to generate a second 3D model of the user for the secondtime.
 16. method of claim 15: wherein initiating the first scan cyclecomprising initiating the first scan cycle at a scanning systemcomprising the platform, the depth sensor, and an actuator systemconfigured to sweep the depth sensor about the path; wherein calculatingthe first set of transforms that align the first sequence of depthimages comprises, at a remote computer system: accessing the firstsequence of depth images from the scanning system; and calculating thefirst set of transforms that align surface profiles represented bypoints in the first sequence of depth images; wherein compiling thesecond set of depth images according to the first set of transforms togenerate the second 3D model of the user comprises, at a mobile deviceproximal the scanning system: accessing the second sequence of depthimages accessing a subset of the first set of transforms from the remotecomputer system; and compiling the second set of depth images accordingto the subset of the first set of transforms to generate the second 3Dmodel of the user.
 17. A method for modeling a human body comprising:initiating a scan cycle while a user occupies a scale platform; duringthe scan cycle: driving a sensor block along a path above the platformand about the user; and recording a sequence of depth images and asequence of color images, via the sensor block, at each capture positionin a sequence of capture positions along the path; compiling the set ofdepth images into a low-density 3D model of the user; compiling the setof depth images into a high-density 3D model of the user; extracting aset of color patches, from the sequence of color images, correspondingto a set of discrete regions on a surface of the low-density 3D model ofthe user; projecting the set of color patches onto the surface of thelow-density 3D model to generate a 3D color model of the user; receivingselection of a dimension on the low-density 3D color model; extracting avalue of the dimension, projected from the low-density 3D color modelonto the high-density 3D model, from the high-density 3D model; andreturning the value of the dimension.
 18. method of claim 17: whereinextracting the set of color patches from the sequence of color imagescomprises: identifying the set of discrete regions on the surface of thelow-density 3D model, each region in the set of discrete regionsapproximating a plane and defining a contiguous surface exceeding aminimum surface area; for each region in the set of discrete regions:identifying a color image, in the sequence of color images, depicting anarea on the user corresponding to the region on the surface of thelow-density 3D model; projecting a boundary of the region onto the colorimage; cropping the color image around the boundary to define a colorpatch for the region; and writing a coordinate of the region, within acoordinate system in the low-density 3D model, to the color patch; andwherein projecting the set of color patches onto the surface of thelow-density 3D model to generate the 3D color model of the usercomprises projecting the set of color patches onto the surface of thelow-density 3D model based on coordinates of the set of color patchesand the coordinate system in the low-density 3D model.
 19. The method ofclaim 17, further comprising, for a first color patch in the set ofcolor patches: detecting a field depicting a homogeneous color patternin the first color patch; predicting a texture type of the field basedon the homogeneous color pattern; retrieving a texture model for thetexture type of the field from a set of predefined texture modelscharacterizing a set of texture types, the texture model comprising a 2Dbump map; and incorporating the texture model into the field in thefirst color patch; and wherein projecting the set of color patches ontothe surface of the low-density 3D model to generate a 3D color model ofthe user comprises, at a mobile device: accessing the low-density 3Dmodel of the user and the set of color patches comprising the firstcolor patch incorporating the texture model; and rendering the set ofcolor patches on the surface of the low-density 3D model to generate the3D color model; and displaying the 3D color model on a display of themobile device.
 20. The method of claim 17: wherein recording thesequence of depth images and the sequence of color images during thescan cycle comprises recording the sequence of depth images, via a depthsensor in the sensor block, at each capture position in the sequence ofcapture positions at a first density along the path; and furthercomprising: initiating a second scan cycle while the user occupies thescale platform at a second time succeeding the scan cycle; during thesecond scan cycle: driving the sensor block along the path; andrecording a second sequence of depth images, via the depth sensor, ateach capture position in a second sequence of capture positions at asecond density less than the first density along the path; compiling theset of depth images into a second low-density 3D model of the user;extracting a second set of color patches, from the second sequence ofcolor images, corresponding discrete regions on a second surface of thesecond low-density 3D model of the user; and projecting the second setof color patches onto the second surface of the second low-density 3Dmodel to generate a second 3D color model of the user at the secondtime.